Dental data mining

ABSTRACT

Systems and methods are disclosed providing a database comprising a compendium of at least one of patient treatment history; orthodontic therapies, orthodontic information and diagnostics; employing a data mining technique for interrogating said database for generating an output data stream, the output data stream correlating a patient malocclusion with an orthodontic treatment; and applying the output data stream to improve a dental appliance or a dental appliance usage.

BACKGROUND

The present invention relates to computational orthodontics anddentistry.

In orthodontic treatment, a patient's teeth are moved from an initial toa final position using any of a variety of appliances. An applianceexerts force on the teeth by which one or more of them are moved or heldin place, as appropriate to the stage of treatment.

SUMMARY

Systems and methods are disclosed providing a database comprising acompendium of at least one of patient treatment history; orthodontictherapies, orthodontic information and diagnostics; employing a datamining technique for interrogating said database for generating anoutput data stream, the output data stream correlating a patientmalocclusion with an orthodontic treatment; and applying the output datastream to improve a dental appliance or a dental appliance usage.

The achieved outcome, if measured, is usually determined using a set ofstandard criteria such as the American Board of Orthodontics againstwhich the final outcome is compared to, and is usually a set ofidealized norms of what the ideal occlusion and bite relationship oughtto be. Another method of determining outcome is to use a relativeimprovement index such as PAR, IOTN, and ICON to measure degrees ofimprovement as a result of treatment.

The present invention provides methods and apparatus for miningrelationships in treatment outcome and use the mined data to enhancetreatment plans or enhance appliance configurations in a process ofrepositioning teeth from an initial tooth arrangement to a final tootharrangement. The invention can operate to define how repositioning isaccomplished by a series of appliances or by a series of adjustments toappliances configured to reposition individual teeth incrementally. Theinvention can be applied advantageously to specify a series ofappliances formed as polymeric shells having the tooth-receivingcavities, that is, shells of the kind described in the above-mentionedU.S. application Ser. No. 09/169276, (Attorney Docket No.018563-004800US-AT-00105US), filed Oct. 8, 1998.

A patient's teeth are repositioned from an initial tooth arrangement toa final tooth arrangement by making a series of incremental positionadjustments using appliances specified in accordance with the invention.In one implementation, the invention is used to specify shapes for theabove-mentioned polymeric shell appliances. The first appliance of aseries will have a geometry selected to reposition the teeth from theinitial tooth arrangement to a first intermediate arrangement. Theappliance is intended to be worn until the first intermediatearrangement is approached or achieved, and then one or more additional(intermediate) appliances are successively placed on the teeth. Thefinal appliance has a geometry selected to progressively repositionteeth from the last intermediate arrangement to a desired final tootharrangement.

The invention specifies the appliances so that they apply an acceptablelevel of force, cause discomfort only within acceptable bounds, andachieve the desired increment of tooth repositioning in an acceptableperiod of time. The invention can be implemented to interact with otherparts of a computational orthodontic system, and in particular tointeract with a path definition module that calculates the paths takenby teeth as they are repositioned during treatment.

In general, in one aspect, the invention provides methods andcorresponding apparatus for segmenting an orthodontic treatment pathinto clinically appropriate substeps for repositioning the teeth of apatient. The methods include providing a digital finite element model ofthe shape and material of each of a sequence of appliances to be appliedto a patient; providing a digital finite element model of the teeth andrelated mouth tissue of the patient; computing the actual effect of theappliances on the teeth by analyzing the finite elements modelscomputationally; and evaluating the effect against clinical constraints.Advantageous implementations can include one or more of the followingfeatures. The appliances can be braces, including brackets andarchwires, polymeric shells, including shells manufactured by stereolithography, retainers, or other forms of orthodontic appliance.Implementations can include comparing the actual effect of theappliances with an intended effect of the appliances; and identifying anappliance as an unsatisfactory appliance if the actual effect of theappliance is more than a threshold different from the intended effect ofthe appliance and modifying a model of the unsatisfactory applianceaccording to the results of the comparison. The model and resultingappliance can be modified by modifying the shape of the unsatisfactoryappliance, by adding a dimple, by adding material to cause anovercorrection of tooth position, by adding a ridge of material toincrease stiffness, by adding a rim of material along a gumline toincrease stiffness, by removing material to reduce stiffness, or byredefining the shape to be a shape defined by the complement of thedifference between the intended effect and the actual effect of theunsatisfactory appliance. The clinical constraints can include a maximumrate of displacement of a tooth, a maximum force on a tooth, and adesired end position of a tooth. The maximum force can be a linear forceor a torsional force. The maximum rate of displacement can be a linearor an angular rate of displacement. The apparatus of the invention canbe implemented as a system, or it can be implemented as a computerprogram product, tangibly stored on a computer-readable medium, havinginstructions operable to cause a computer to perform the steps of themethod of the invention.

Among the advantages of the invention are one or more of the following.Appliances specified in accordance with the invention apply no more thanorthodontically acceptable levels of force, cause no more than anacceptable amount of patient discomfort, and achieve the desiredincrement of tooth repositioning in an acceptable period of time. Theinvention can be used to augment a computational or manual process fordefining tooth paths in orthodontic treatment by confirming thatproposed paths can be achieved by the appliance under consideration andwithin user-selectable constraints of good orthodontic practice. Use ofthe invention to design aligners allows the designer (human orautomated) to finely tune the performance of the aligners with respectto particular constraints. Also, more precise orthodontic control overthe effect of the aligners can be achieved and their behavior can bebetter predicted than would otherwise be the case. In addition,computationally defining the aligner geometry facilitates direct alignermanufacturing under numerical control.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features andadvantages of the invention will become apparent from the description,the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows one exemplary dental data mining system.

FIG. 1B shows an analysis of the performance of one or more dentalappliances.

FIG. 1C shows various Movement Type data used in one embodiment of thedata mining system.

FIG. 1D shows an analysis of the performance of one or more dentalappliances.

FIGS. 1E-1F show various embodiments of a clusterizer to generatetreatment plans.

FIG. 2A is a flowchart of a process of specifying a course of treatmentincluding a subprocess for calculating aligner shapes in accordance withthe invention.

FIG. 2B is a flowchart of a process for calculating aligner shapes.

FIG. 3 is a flowchart of a subprocess for creating finite elementmodels.

FIG. 4 is a flowchart of a subprocess for computing aligner changes.

FIG. 5A is a flowchart of a subprocess for calculating changes inaligner shape.

FIG. 5B is a flowchart of a subprocess for calculating changes inaligner shape.

FIG. 5C is a flowchart of a subprocess for calculating changes inaligner shape.

FIG. 5D is a schematic illustrating the operation of the subprocess ofFIG. 5B.

FIG. 6 is a flowchart of a process for computing shapes for sets ofaligners.

FIG. 7 is an exemplary diagram of a statistical root model.

FIG. 8 are exemplary diagrams of root modeling.

FIG. 9 are exemplary diagrams of CT scan of teeth.

FIG. 10 shows an exemplary user interface showing teeth.

FIG. 11 shows the exemplary diagram of FIG. 10 with root data.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION OF THE INVENTION

F Digital treatment plans are now possible with 3-dimensionalorthodontic treatment planning tools such as ClinCheck® from AlignTechnology, Inc. or other software available from eModels and OrthoCAD,among others. These technologies allow the clinician to use the actualpatient's dentition as a starting point for customizing the treatmentplan. The ClinCheck® technology uses a patient-specific digital model toplot a treatment plan, and then use a scan of the achieved treatmentoutcome to assess the degree of success of the outcome as compared tothe original digital treatment plan (previously filed patent for thistechnology—superimposition tool). The problem with the digital treatmentplan and outcome assessment is the abundance of data and the lack ofstandards and efficient methodology by which to assess “treatmentsuccess” at a individual patient level. To analyze the information, adental data mining system is used.

FIG. 1A shows one exemplary dental data mining system. In this system,dental treatment and outcome data sets 1 are stored in a database orinformation warehouse 2. The data is extracted by a data mining software3 that generates results 4. The data mining software can interrogate theinformation captured and/or updated in the database 2 and can generatean output data stream correlating a patient tooth problem with a dentalappliance solution. Note that the output of the data mining software canbe most advantageously, self-reflexively, fed as a subsequent input toat least the database and the data mining correlation algorithm.

The result of the data mining system of FIG. 1 is used for definingappliance configurations or changes to appliance configurations forincrementally moving teeth. The tooth movements will be those normallyassociated with orthodontic treatment, including translation in allthree orthogonal directions relative to a vertical centerline, rotationof the tooth centerline in the two orthodontic directions (“rootangulation” and “torque”), as well as rotation about the centerline.

In one embodiment, the data mining system captures the plan, the startposition and the final dental position. The system compares the outcometo the plan, using any treatment methodology including removableappliances as well as fixed appliances such as orthodontic brackets andwires, or even other dental treatment such as comparing achieved to planfor orthognathic surgery (may be patents out there because there existssoftware that compares outcome facial profile to predictive 2-D images),periodontics, restorative, among others.

In one embodiment, a teeth superimposition tool is used to matchtreatment files of each arch scan. The refinement scan is superimposedover the initial one to arrive at a match based upon tooth anatomy andtooth coordinate system. After teeth in the two arches are matched, thesuperimposition tool asks for a reference in order to relate the upperarch to the lower arch. When the option “statistical filtering” isselected, the superimposition tool measures the amount of movement foreach tooth by first eliminating as reference the ones that move(determined by the difference in position between the current stage andthe previous one) more than one standard deviation either above or belowthe mean of movement of all teeth. The remaining teeth are then selectedas reference to measure movement of each tooth.

FIG. 1B shows an analysis of the performance of one or more dentalappliances. “Achieved” is plotted against “Goal” in scatter graphs, andtrend lines are generated. Scatter graphs are shown to demonstrate whereall “scattered” data points are, and trend lines are generated to showthe performance of the dental appliances. In one embodiment, trend linesare selected to be linear (they can be curvilinear); thus trend linespresent as the “best fit” straight lines for all “scattered” data. Theperformance of the Aligners is represented as the slope of a trend line.The Y axis intercept is the incidental movement that occurs when wearingthe Aligners. Predictability is measured by R2 that is obtained from aregression computation of “Achieved” and “Goal.” data. A number ofscatter graphs are shown below.

FIG. 1C shows various Movement Type data used in one embodiment of thedata mining system. Exemplary data sets cover Expansion/Constriction(+/−X Translation), Mesialization/Distalization (+/−Y Translation),Intrusion (−Z Translation), Extrusion (+Z Translation), Tip/Angulation(X Rotation), Torque/Inclination (Y Rotation), and Pure Rotation (ZRotation).

FIG. 1D shows an analysis of the performance of one or more dentalappliances. FIG. 1D shows that Incisor Intrusions are well controlled,that is, the target goal is achieved about 85% of the time for thatparticular set of data.

As illustrated saliently in FIG. 1D, actual tooth movement generallylags targeted tooth movement at many stages. In the case of treatmentwith sequences of polymer appliances, such lags play and important rolein treatment design, because both tooth movement and such negativeoutcomes as patient discomfort vary positively with the extent of thediscrepancies.

In one embodiment, clinical parameters in steps such as 170 and 232 aremade more precise and safer by allowing for the statistical deviation oftargeted from actual tooth position. For example, a subsequent movementtarget might be reduced because of a large calculated probability ofcurrently targeted tooth movement not having been achieved adequately,with the result that there is a high probability the subsequent movementstage will need to complete work intended for an earlier stage.Similarly, targeted movement might overshoot desired positionsespecially in earlier stages so that expected actual movement is bettercontrolled. This embodiment sacrifices the goal of minimizing round triptime in favor of achieving a higher probability of targeted end-stageoutcome. This methodology is accomplished within treatment plansspecific to clusters of similar patient cases.

Table 1 shows grouping of teeth in one embodiment. The sign conventionof tooth movements is indicated in Table 2. Different tooth movements ofthe selected 60 arches were demonstrated in Table 3 with performancesorted by descending order. The appliance performance can be broken into4 separate groups: high (79-85%), average (60-68%), below average(52-55%), and inadequate (24-47%). Table 4 shows ranking ofpredictability. Predictability is broken into 3 groups: highlypredictable (0.76-0.82), predictable (0.43-0.63) and unpredictable(0.10-0.30). For the particular set of data, the findings are asfollows:

1. Incisor intrusion, and anterior intrusion performance are high. Therange for incisor intrusion is about 1.7 mm, and for anterior intrusionis about 1.7 mm. These movements are highly predictable.

2. Canine intrusion, incisor torque, incisor rotation and anteriortorque performance are average. The range for canine intrusion is about1.3 mm, for incisor torque is about 34 degrees, for incisor rotation isabout 69 degrees, and for anterior torque is about 34 degrees. Thesemovements are either predictable or highly predictable.

3. Bicuspid tipping, bicuspid mesialization, molar rotation, andposterior expansion performance are below average. The range forbicuspid mesialization is about 1 millimeter, for bicuspid tipping isabout 19 degrees, for molar rotation is about 27 degrees and forposterior expansion is about 2.8 millimeters. Bicuspid tipping andmesialization are unpredictable. Whereas the rest are predictablemovements.

4. Anterior and incisor extrusion, round teeth and bicuspid rotation,canine tipping, molar distalization, posterior torque performance areinadequate. The range of anterior extrusion is about 1.7 millimeters,for incisor extrusion is about 1.5 mm, for round teeth rotation is about67 degrees for bicuspid rotation is about 63 degrees, for canine tippingis about 26 degrees, for molar distalization is about 2 millimeters, andfor posterior torque is about 43 degrees. All are unpredictable movementexcept bicuspid rotation which is predictable. TABLE 1 Studied groups ofteeth Teeth Incisors #7, 8, 9, 10, 23, 24, 25, 26 Canines #6, 11, 22, 27Bicuspids #4, 5, 12, 13, 20, 21, 28, 29 Molars #2, 3, 14, 15, 18, 19,30, 31 Anteriors #6, 7, 8, 9, 10, 11, 22, 23, 24, 25, 26, 27 Posteriors#2, 3, 4, 5, 12, 13, 14, 15, 18, 19, 20, 21, 28, 29, 30, 31 Round #4, 5,6, 11, 12, 13, 20, 21, 22, 27, 28, 29

TABLE 2 Sign convention of tooth movements Type of Movement Xtranslation (−) is lingual (+) is buccal (Expansion/ Constriction) Xrotation (Tipping) Upper & Lower (−) is distal (+) is mesial rightquadrants Upper & Lower (−) is mesial (+) is distal left quadrants Ytranslation (Mesialization/ Distalization) Upper left & Lower (−) isdistal (+) is mesial right quadrants Upper right & Lower (−) is mesial(+) is distal left quadrants Y rotation (−) is lingual crown (+) isbuccal crown (Torquing) Z translation (−) is intrusion (+) is extrusion(Intrusion/Extrusion) Z rotation (−) is clockwise (+) iscounterclockwise (Pure Rotation)

TABLE 4 Ranking of Performance Index of movement Performance SidePredicta- Group Movement Model Index Effect bility Incisor IntrusionLinear 85% 0.03 0.82 Anterior Intrusion Linear 79% 0.03 0.76 CanineIntrusion Linear 68% −0.10 0.43 Incisor Torque Linear 67% 0.21 0.63Anterior Torque Linear 62% 0.15 0.56 Incisor Rotation Linear 61% −0.090.76 Bicuspid Tipping Linear 55% 0.35 0.27 Molar Rotation Linear 52%0.11 0.58 Posterior Expansion Linear 52% 0.11 0.48 BicuspidMesialization Linear 52% 0.00 0.30 Bicuspid Rotation Linear 47% 0.280.63 Molar Distalization Linear 43% 0.02 0.20 Canine Tipping Linear 42%0.10 0.28 Posterior Torque Linear 42% 1.50 0.28 Round Rotation Linear39% −0.14 0.27 Anterior Extrusion Linear 29% −0.02 0.13 IncisorExtrusion Linear 24% 0.02 0.10

TABLE 4 Ranking of movement predictability Performance Side Predicta-Group Movement Model Index Effect bility Incisor Intrusion Linear 85%0.03 0.82 Anterior Intrusion Linear 79% 0.03 0.76 Incisor RotationLinear 61% −0.09 0.76 Incisor Torque Linear 67% 0.21 0.63 BicuspidRotation Linear 47% 0.28 0.63 Molar Rotation Linear 52% 0.11 0.58Anterior Torque Linear 62% 0.15 0.56 Posterior Expansion Linear 52% 0.110.48 Canine Intrusion Linear 68% −0.10 0.43 Bicuspid MesializationLinear 52% 0.00 0.30 Canine Tipping Linear 42% 0.10 0.28 PosteriorTorque Linear 42% 1.50 0.28 Bicuspid Tipping Linear 55% 0.35 0.27 RoundRotation Linear 39% −0.14 0.27 Molar Distalization Linear 43% 0.02 0.20Anterior Extrusion Linear 29% −0.02 0.13 Incisor Extrusion Linear 24%0.02 0.10

In one embodiment, data driven analyzers may be applied. These datadriven analyzers may incorporate a number of models such as parametricstatistical models, non-parametric statistical models, clusteringmodels, nearest neighbor models, regression methods, and engineered(artificial) neural networks. Prior to operation, data driven analyzersor models are built using one or more training sessions. The data usedto build the analyzer or model in these sessions are typically referredto as training data. As data driven analyzers are developed by examiningonly training examples, the selection of the training data cansignificantly affect the accuracy and the learning speed of the datadriven analyzer. One approach used heretofore generates a separate dataset referred to as a test set for training purposes. The test set isused to avoid overfitting the model or analyzer to the training data.Overfitting refers to the situation where the analyzer has memorized thetraining data so well that it fails to fit or categorize unseen data.Typically, during the construction of the analyzer or model, theanalyzer's performance is tested against the test set. The selection ofthe analyzer or model parameters is performed iteratively until theperformance of the analyzer in classifying the test set reaches anoptimal point. At this point, the training process is completed. Analternative to using an independent training and test set is to use amethodology called cross-validation. Cross-validation can be used todetermine parameter values for a parametric analyzer or model for anon-parametric analyzer. In cross-validation, a single training data setis selected. Next, a number of different analyzers or models are builtby presenting different parts of the training data as test sets to theanalyzers in an iterative process. The parameter or model structure isthen determined on the basis of the combined performance of all modelsor analyzers. Under the cross-validation approach, the analyzer or modelis typically retrained with data using the determined optimal modelstructure.

In one embodiment, the data mining software 3 can be a “spider” or“crawler” to grab data on the database 2 for indexing. In oneembodiment, clustering operations are performed to detect patterns inthe data. In another embodiment, a neural network is used to recognizeeach pattern as the neural network is quite robust at recognizing dentaltreatment patterns. Once the treatment features have been characterized,the neural network then compares the input dental information withstored templates of treatment vocabulary known by the neural networkrecognizer, among others. The recognition models can include a HiddenMarkov Model (HMM), a dynamic programming model, a neural network, afuzzy logic, or a template matcher, among others. These models may beused singly or in combination.

Dynamic programming considers all possible points within the permitteddomain for each value of i. Because the best path from the current pointto the next point is independent of what happens beyond that point.Thus, the total cost of [i(k), j(k)] is the cost of the point itselfplus the cost of the minimum path to it. Preferably, the values of thepredecessors can be kept in an M×N array, and the accumulated cost keptin a 2×N array to contain the accumulated costs of the immediatelypreceding column and the current column. However, this method requiressignificant computing resources.

Dynamic programming requires a tremendous amount of computation. For therecognizer to find the optimal time alignment between a sequence offrames and a sequence of node models, it must compare most framesagainst a plurality of node models. One method of reducing the amount ofcomputation required for dynamic programming is to use pruning. Pruningterminates the dynamic programming of a given portion of dentaltreatment information against a given treatment model if the partialprobability score for that comparison drops below a given threshold.This greatly reduces computation.

Considered to be a generalization of dynamic programming, a hiddenMarkov model is used in the preferred embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable.

In the preferred embodiment, the Markov network is used to model anumber of dental treatment options. The transitions between states arerepresented by a transition matrix A=[a(i,j)]. Each a(i,j) term of thetransition matrix is the probability of making a transition to state jgiven that the model is in state i. The output symbol probability of themodel is represented by a set of functions B=[b(j) (O(t)], where theb(j) (O(t) term of the output symbol matrix is the probability ofoutputting observation O(t), given that the model is in state j. Thefirst state is always constrained to be the initial state for the firsttime frame of the utterance, as only a prescribed set of left to rightstate transitions are possible. A predetermined final state is definedfrom which transitions to other states cannot occur.

Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. For example, a treatment pattern currently having a frameof feature signals in state 2 has a probability of reentering state 2 ofa(2,2), a probability a(2,3) of entering state 3 and a probability ofa(2,4)=1 a(2,1) a(2,2) of entering state 4. The probability a(2,1) ofentering state 1 or the probability a(2,5) of entering state 5 is zeroand the sum of the probabilities a(2,1) through a(2,5) is one. Althoughthe preferred embodiment restricts the flow graphs to the present stateor to the next two states, one skilled in the art can build an HMM modelwithout any transition restrictions, although the sum of all theprobabilities of transitioning from any state must still add up to one.

In each state of the model, the current feature frame may be identifiedwith one of a set of predefined output symbols or may be labeledprobabilistically. In this case, the output symbol probability b(j) O(t)corresponds to the probability assigned by the model that the featureframe symbol is O(t). The model arrangement is a matrix A=[a(i,j)] oftransition probabilities and a technique of computing B=b(j) O(t), thefeature frame symbol probability in state j.

The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The dentaltreatment information traverses through the feature extractor. Duringlearning, the resulting feature vector series is processed by aparameter estimator, whose output is provided to the hidden Markovmodel. The hidden Markov model is used to derive a set of referencepattern templates, each template representative of an identified patternin a vocabulary set of reference treatment patterns. The Markov modelreference templates are next utilized to classify a sequence ofobservations into one of the reference patterns based on the probabilityof generating the observations from each Markov model reference patterntemplate. During recognition, the unknown pattern can then be identifiedas the reference pattern with the highest probability in the likelihoodcalculator.

The HMM template has a number of states, each having a discrete value.However, because treatment pattern features may have a dynamic patternin contrast to a single value. The addition of a neural network at thefront end of the HMM in an embodiment provides the capability ofrepresenting states with dynamic values. The input layer of the neuralnetwork comprises input neurons. The outputs of the input layer aredistributed to all neurons in the middle layer. Similarly, the outputsof the middle layer are distributed to all output states, which normallywould be the output layer of the neuron. However, each output hastransition probabilities to itself or to the next outputs, thus forminga modified HMM. Each state of the thus formed HMM is capable ofresponding to a particular dynamic signal, resulting in a more robustHMM. Alternatively, the neural network can be used alone withoutresorting to the transition probabilities of the HMM architecture.

The output streams or results 4 of FIG. 1A are used as feedback inimproving dental appliance design and/or usage by doctors. For example,the data mining results can be used to evaluate performance based onstaging approaches, to compare appliance performance indices based ontreatment approaches, and to evaluate performance comparing differentattachment shapes and positions on teeth.

The ability to study tooth-specific efficacy and product performance forlarge clusters of treatment outcomes enables statistically significantcomparisons to be made between two or more populations of cases. In theevent that the two clusters studied contain differences in treatmentapproach, appliance design, or manufacturing protocol, the differencesseen in the performance of the product as exhibited by the data output,can be attributed to the approach, design, or manufacturing protocol.The end result is a feedback mechanism that enables either the clinicianor the manufacturer the ability to optimize the product design and usagebased on performance data from a significantly large sample size usingobjective measurable data.

The theory of orthodontic treatment is not universally agreed upon, andactual treatment and outcomes are subject to additional uncertainties ofmeasurement of patient variables, of relationships to unmeasured patientvariables, as well as of varying patient compliance. As a result,different clinicians might prefer different treatment plans for a singlepatient. Thus, a single treatment plan may not be accepted by everyclinician since there is no universally accepted “correct” treatmentplan.

The next few embodiments allow greater clinician satisfaction andgreater patient satisfaction by tailoring treatment parameters topreferences of clinicians. The system detects differences in treatmentpreferences by statistical observation of the treatment histories ofclinicians. For example, clinicians vary in how likely they would be toperform bicuspid extraction in cases with comparable crowding. Even whenthere is not a sufficient record of past treatments for a givenclinician, clustering may be performed on other predictor variables suchas geographical location, variables related to training, or size andnature of practice, to observe statistically significant differences intreatment parameters.

Data mining can discover statistically significant patterns of differenttreatment outcome achieved by different clinicians for comparablepatients. For example, patient cases clustered together might havesystematically fewer complications with one clinician as compared toanother. Such a difference detected by the data mining tool might beused as a flag for feedback to the more poorly performing clinician aswell as a flag for solicitation of treatment differences used by thebetter performing clinician.

In one embodiment, clustering techniques are used with previouslycompleted cases to categorize treatment complications and outcomes.Probability models of risk are then built within each cluster. New casesare then allocated to the same clusters based on similarity ofpre-treatment variables. The risks within each cluster of patients withcompleted treatments are then used with new cases to predict treatmentoutcomes and risks of complications. High-risk patients are then flaggedfor special attention, possibly including additional steps in treatmentplan or additional clinical intervention.

In another embodiment, practitioners are clustered into groups byobserved clinician treatment preferences, and treatment parameters areadjusted within each group to coincide more closely with observedtreatment preferences. Practitioners without observed histories are thenassigned to groups based on similarity of known variables to thosewithin clusters with known treatment histories.

FIG. 1E shows an exemplary process for clusterizing practices. First,the process clusterizes treatment practice based on clinician treatmenthistory such as treatment preferences, outcomes, and demographic &practice variables (20). Next, the system models preferred clinicalconstraints within each cluster (22). Next, the system assignsclinicians without treatment history to clusters in 20 based ondemographic and practice variables (24). In one embodiment, the systemperforms process 100 separately within each cluster, usingcluster-specific clinical constraints (26). Additionally, the systemupdate clusters and cluster assignment as new treatment and outcomesdata arrive (28).

FIG. 1F shows another embodiment of a data mining system to generateproposed treatments. First, the system identifies/clusterizes patienthistories having detailed follow-up (such as multiple high-resolutionscans), based on detailed follow-up data, diagnosis, treatmentparameters and outcomes, and demographic variables (40). Within eachcluster, the system models discrepancies between intended position andactual positions obtained from follow-up data (42). Further, within eachcluster, the system models risk for special undesirable outcomes (44).Patient histories are clusterized with less detailed follow-up databased on available variables. Assign to clusters calculated in 40 (46).The system refines step 42 models with additional records from step 46clusters (48). It can also refine step 44 models with additional recordsfrom step 48 clusters (50). The system then assigns new patients to step46 clusters based on diagnosis, demographic, and initial physical (52).Within each step 52 cluster, the system models expected discrepanciesbetween intended position and actual positions (54). From step 54, thesystem uses revised expected position information where relevant(including 232, 250) (67). Additionally, within each step 52 cluster,the system models risk for undesirable outcomes (56). From step 56, thesystem also flags cases that require special attention and clinicalconstraints (as in 204, 160) (69). The process then customizes treatmentplan to each step 52 cluster (58). Next, the system iteratively collectsdata (61) and loops back to (40). The system also continually identifiesclusters without good representation in step 40 clusters for additionalfollow-up analysis (65).

In clinical treatment settings, it is not cost-effective to obtain orprocess the full high-resolution data possible at every stage of toothmovement. For example:

-   -   Patients may use several appliances between visits to        clinicians.    -   A given patient may submit only one set of tooth impressions.    -   Radiation concerns may limit the number of CT or X-Ray scans        used.    -   Clinicians generally do not have the time to report detailed        spatial information on each tooth at each visit.

Due to these and other limitation, treatment planning is necessarilymade based on partial information.

In one embodiment, such missing information is approximatedsubstantially by matching predictive characteristics between patientsand a representative sample for which detailed follow-up information iscollected. In this case, patients are flagged based on poorlyanticipated treatment outcomes for requests for follow-up information,such as collection and analysis of additional sets of tooth impressions.Resulting information is then used to refine patient clusters andtreatment of patients later assigned to the clusters.

In general, patient data is scanned and the data is analyzed using thedata mining system described above. A treatment plan is proposed by thesystem for the dental practitioner to approve. The dental practitionercan accept or request modifications to the treatment plan. Once thetreatment plan is approved, manufacturing of appliance(s) can begin.

FIG. 2A illustrates the general flow of an exemplary process 100 fordefining and generating repositioning appliances for orthodontictreatment of a patient. The process 100 includes the methods, and issuitable for the apparatus, of the present invention, as will bedescribed. The computational steps of the process are advantageouslyimplemented as computer program modules for execution on one or moreconventional digital computers.

As an initial step, a mold or a scan of patient's teeth or mouth tissueis acquired (110). This step generally involves taking casts of thepatient's teeth and gums, and may also involve taking wax bites, directcontact scanning, x-ray imaging, tomographic imaging, sonographicimaging, and other techniques for obtaining information about theposition and structure of the teeth, jaws, gums and otherorthodontically relevant tissue. From the data so obtained, a digitaldata set is derived that represents the initial (that is, pretreatment)arrangement of the patient's teeth and other tissues.

The initial digital data set, which may include both raw data fromscanning operations and data representing surface models derived fromthe raw data, is processed to segment the tissue constituents from eachother (step 120). In particular, in this step, data structures thatdigitally represent individual tooth crowns are produced.Advantageously, digital models of entire teeth are produced, includingmeasured or extrapolated hidden surfaces and root structures.

The desired final position of the teeth—that is, the desired andintended end result of orthodontic treatment—can be received from aclinician in the form of a prescription, can be calculated from basicorthodontic principles, or can be extrapolated computationally from aclinical prescription (step 130). With a specification of the desiredfinal positions of the teeth and a digital representation of the teeththemselves, the final position and surface geometry of each tooth can bespecified (step 140) to form a complete model of the teeth at thedesired end of treatment. Generally, in this step, the position of everytooth is specified. The result of this step is a set of digital datastructures that represents an orthodontically correct repositioning ofthe modeled teeth relative to presumed-stable tissue. The teeth andtissue are both represented as digital data.

Having both a beginning position and a final position for each tooth,the process next defines a tooth path for the motion of each tooth. Thetooth paths are optimized in the aggregate so that the teeth are movedin the quickest fashion with the least amount of round-tripping to bringthe teeth from their initial positions to their desired final positions.(Round-tripping is any motion of a tooth in any direction other thandirectly toward the desired final position. Round-tripping is sometimesnecessary to allow teeth to move past each other.) The tooth paths aresegmented. The segments are calculated so that each tooth's motionwithin a segment stays within threshold limits of linear and rotationaltranslation. In this way, the end points of each path segment canconstitute a clinically viable repositioning, and the aggregate ofsegment end points constitute a clinically viable sequence of toothpositions, so that moving from one point to the next in the sequencedoes not result in a collision of teeth.

The threshold limits of linear and rotational translation areinitialized, in one implementation, with default values based on thenature of the appliance to be used. More individually tailored limitvalues can be calculated using patient-specific data. The limit valuescan also be updated based on the result of an appliance-calculation(step 170, described later), which may determine that at one or morepoints along one or more tooth paths, the forces that can be generatedby the appliance on the then-existing configuration of teeth and tissueis incapable of effecting the repositioning that is represented by oneor more tooth path segments. With this information, the subprocessdefining segmented paths (step 150) can recalculate the paths or theaffected subpaths.

At various stages of the process, and in particular after the segmentedpaths have been defined, the process can, and generally will, interactwith a clinician responsible for the treatment of the patient (step160). Clinician interaction can be implemented using a client processprogrammed to receive tooth positions and models, as well as pathinformation from a server computer or process in which other steps ofprocess 100 are implemented. The client process is advantageouslyprogrammed to allow the clinician to display an animation of thepositions and paths and to allow the clinician to reset the finalpositions of one or more of the teeth and to specify constraints to beapplied to the segmented paths. If the clinician makes any such changes,the subprocess of defining segmented paths (step 150) is performedagain.

The segmented tooth paths and associated tooth position data are used tocalculate clinically acceptable appliance configurations (or successivechanges in appliance configuration) that will move the teeth on thedefined treatment path in the steps specified by the path segments (step170). Each appliance configuration represents a step along the treatmentpath for the patient. The steps are defined and calculated so that eachdiscrete position can follow by straight-line tooth movement or simplerotation from the tooth positions achieved by the preceding discretestep and so that the amount of repositioning required at each stepinvolves an orthodontically optimal amount of force on the patient'sdentition. As with the path definition step, this appliance calculationstep can include interactions and even iterative interactions with theclinician (step 160). The operation of a process step 200 implementingthis step will be described more fully below.

Having calculated appliance definitions, the process 100 can proceed tothe manufacturing step (step 180) in which appliances defined by theprocess are manufactured, or electronic or printed information isproduced that can be used by a manual or automated process to defineappliance configurations or changes to appliance configurations.

FIG. 2B illustrates a process 200 implementing the appliance-calculationstep (FIG. 2A, step 170) for polymeric shell aligners of the kinddescribed in above-mentioned patent application Ser. No. 09/169,276(attorney docket no. 018563- 004800). Inputs to the process include aninitial aligner shape 202, various control parameters 204, and a desiredend configuration for the teeth at the end of the current treatment pathsegment 206. Other inputs include digital models of the teeth inposition in the jaw, models of the jaw tissue, and specifications of aninitial aligner shape and of the aligner material. Using the input data,the process creates a finite element model of the aligner, teeth andtissue, with the aligner in place on the teeth (step 210). Next, theprocess applies a finite element analysis to the composite finiteelement model of aligner, teeth and tissue (step 220). The analysis runsuntil an exit condition is reached, at which time the process evaluateswhether the teeth have reached the desired end position for the currentpath segment, or a position sufficiently close to the desired endposition (step 230). If an acceptable end position is not reached by theteeth, the process calculates a new candidate aligner shape (step 240).If an acceptable end position is reached, the motions of the teethcalculated by the finite elements analysis are evaluated to determinewhether they are orthodontically acceptable (step 232). If they are not,the process also proceeds to calculate a new candidate aligner shape(step 240). If the motions are orthodontically acceptable and the teethhave reached an acceptable position, the current aligner shape iscompared to the previously calculated aligner shapes. If the currentshape is the best solution so far (decision step 250), it is saved asthe best candidate so far (step 260). If not, it is saved in an optionalstep as a possible intermediate result (step 252). If the currentaligner shape is the best candidate so far, the process determineswhether it is good enough to be accepted (decision step 270). If it is,the process exits. Otherwise, the process continues and calculatesanother candidate shape (step 240) for analysis.

The finite element models can be created using computer programapplication software available from a variety of vendors. For creatingsolid geometry models, computer aided engineering (CAE) or computeraided design (CAD) programs can be used, such as the AutoCAD® softwareproducts available from Autodesk, Inc., of San Rafael, Calif. Forcreating finite element models and analyzing them, program products froma number of vendors can be used, including the PolyFEM product availablefrom CADSI of Coralville, Iowa, the Pro/Mechanica simulation softwareavailable from Parametric Technology Corporation of Waltham, Mass., theI-DEAS design software products available from Structural DynamicsResearch Corporation (SDRC) of Cincinnati, Ohio, and the MSC/NASTRANproduct available from MacNeal-Schwendler Corporation of Los Angeles,Calif.

FIG. 3 shows a process 300 of creating a finite element model that canbe used to perform step 210 of the process 200 (FIG. 2). Input to themodel creation process 300 includes input data 302 describing the teethand tissues and input data 304 describing the aligner. The input datadescribing the teeth 302 include the digital models of the teeth;digital models of rigid tissue structures, if available; shape andviscosity specifications for a highly viscous fluid modeling thesubstrate tissue in which the teeth are embedded and to which the teethare connected, in the absence of specific models of those tissues; andboundary conditions specifying the immovable boundaries of the modelelements. In one implementation, the model elements include only modelsof the teeth, a model of a highly viscous embedding substrate fluid, andboundary conditions that define, in effect, a rigid container in whichthe modeled fluid is held. Note that fluid characteristics may differ bypatient clusters, for example as a function of age.

A finite element model of the initial configuration of the teeth andtissue is created (step 310) and optionally cached for reuse in lateriterations of the process (step 320). As was done with the teeth andtissue, a finite element model is created of the polymeric shell aligner(step 330). The input data for this model includes data specifying thematerial of which the aligner is made and the shape of the aligner (datainput 304).

The model aligner is then computationally manipulated to place it overthe modeled teeth in the model jaw to create a composite model of anin-place aligner (step 340). Optionally, the forces required to deformthe aligner to fit over the teeth, including any hardware attached tothe teeth, are computed and used as a figure of merit in measuring theacceptability of the particular aligner configuration. Optionally, thetooth positions used are as estimated from a probabilistic model basedon prior treatment steps and other patient information. In a simpleralternative, however, the aligner deformation is modeled by applyingenough force to its insides to make it large enough to fit over theteeth, placing the model aligner over the model teeth in the compositemodel, setting the conditions of the model teeth and tissue to beinfinitely rigid, and allowing the model aligner to relax into positionover the fixed teeth. The surfaces of the aligner and the teeth aremodeled to interact without friction at this stage, so that the alignermodel achieves the correct initial configuration over the model teethbefore finite element analysis is begun to find a solution to thecomposite model and compute the movement of the teeth under theinfluence of the distorted aligner.

FIG. 4 shows a process 400 for calculating the shape of a next alignerthat can be used in the aligner calculations, step 240 of process 200(FIG. 2). A variety of inputs are used to calculate the next candidatealigner shape. These include inputs 402 of data generated by the finiteelement analysis solution of the composite model and data 404 defined bythe current tooth path. The data 402 derived from the finite elementanalysis includes the amount of real elapsed time over which thesimulated repositioning of the teeth took place; the actual end toothpositions calculated by the analysis; the maximum linear and torsionalforce applied to each tooth; the maximum linear and angular velocity ofeach tooth. From the input path information, the input data 404 includesthe initial tooth positions for the current path segment, the desiredtooth positions at the end of the current path segment, the maximumallowable displacement velocity for each tooth, and the maximumallowable force of each kind for each tooth.

If a previously evaluated aligner was found to violate one or moreconstraints, additional input data 406 can optionally be used by theprocess 400. This data 406 can include information identifying theconstraints violated by, and any identified suboptimal performance of,the previously evaluated aligner.

Having received the initial input data (step 420), the process iteratesover the movable teeth in the model. (Some of the teeth may beidentified as, and constrained to be, immobile.) If the end position anddynamics of motion of the currently selected tooth by the previouslyselected aligner is acceptable (“yes” branch of decision step 440), theprocess continues by selecting for consideration a next tooth (step 430)until all teeth have been considered (“done” branch from step 430 tostep 470). Otherwise (“no” branch from step 440), a change in thealigner is calculated in the region of the currently selected tooth(step 450). The process then moves back to select the next current tooth(step 430) as has been described.

When all of the teeth have been considered, the aggregate changes madeto the aligner are evaluated against previously defined constraints(step 470), examples of which have already been mentioned. Constraintscan be defined with reference to a variety of further considerations,such as manufacturability. For example, constraints can be defined toset a maximum or minimum thickness of the aligner material, or to set amaximum or minimum coverage of the aligner over the crowns of the teeth.If the aligner constraints are satisfied, the changes are applied todefine a new aligner shape (step 490). Otherwise, the changes to thealigner are revised to satisfy the constraints (step 480), and therevised changes are applied to define the new aligner shape (step 490).

FIG. 5A illustrates one implementation of the step of computing analigner change in a region of a current tooth (step 450). In thisimplementation, a rule-based inference engine 456 is used to process theinput data previously described (input 454) and a set of rules 452 a-452n in a rule base of rules 452. The inference engine 456 and the rules452 define a production system which, when applied to the factual inputdata, produces a set of output conclusions that specify the changes tobe made to the aligner in the region of the current tooth (output 458).

Rules 452 have the conventional two-part form: an if-part defining acondition and a then-part defining a conclusion or action that isasserted if the condition is satisfied. Conditions can be simple or theycan be complex conjunctions or disjunctions of multiple assertions. Anexemplary set of rules, which defines changes to be made to the aligner,includes the following: if the motion of the tooth is too slow, adddriving material to the aligner opposite the desired direction ofmotion; if the motion of the tooth is too slow, add driving material toovercorrect the position of the tooth; if the tooth is too far short ofthe desired end position, add material to overcorrect; if the tooth hasbeen moved too far past the desired end position, add material tostiffen the aligner where the tooth moves to meet it; if a maximumamount of driving material has been added, add material to overcorrectthe repositioning of the tooth and do not add driving material; if themotion of the tooth is in a direction other than the desired direction,remove and add material so as to redirect the tooth.

In an alternative embodiment, illustrated in FIGS. 5B and 5C, anabsolute configuration of the aligner is computed, rather than anincremental difference. As shown in FIG. 5B, a process 460 computes anabsolute configuration for an aligner in a region of a current tooth.Using input data that has already been described, the process computesthe difference between the desired end position and the achieved endposition of the current tooth (462). Using the intersection of the toothcenter line with the level of the gum tissue as the point of reference,the process computes the complement of the difference in all six degreesof freedom of motion, namely three degrees of translation and threedegrees of rotation (step 464). Next, the model tooth is displaced fromits desired end position by the amounts of the complement differences(step 466), which is illustrated in FIG. 5D.

FIG. 5D shows a planar view of an illustrative model aligner 60 over anillustrative model tooth 62. The tooth is in its desired end positionand the aligner shape is defined by the tooth in this end position. Theactual motion of the tooth calculated by the finite element analysis isillustrated as placing the tooth in position 64 rather than in thedesired position 62. A complement of the computed end position isillustrated as position 66. The next step of process 460 (FIG. 5B)defines the aligner in the region of the current tooth in this iterationof the process by the position of the displaced model tooth (step 468)calculated in the preceding step (466). This computed alignerconfiguration in the region of the current tooth is illustrated in FIG.5D as shape 68 which is defined by the repositioned model tooth inposition 66.

A further step in process 460, which can also be implemented as a rule452 (FIG. 5A), is shown in FIG. 5C. To move the current tooth in thedirection of its central axis, the size of the model tooth defining thatregion of the aligner, or the amount of room allowed in the aligner forthe tooth, is made smaller in the area away from which the process hasdecided to move the tooth (step 465).

As shown in FIG. 6, the process 200 of computing the shape for analigner for a step in a treatment path is one step in an overall process600 of computing the shapes of a series of aligners. This overallprocess 600 begins with an initialization step 602 in which initialdata, control and constraint values are obtained.

When an aligner configuration has been found for each step or segment ofthe treatment path (step 604), the overall process 600 determineswhether all of the aligners are acceptable (step 606). If they are, theprocess exits and is complete. Otherwise, the process optionallyundertakes a set of steps 610 in an attempt to calculate a set ofacceptable aligners. First, one or more of the constraints on thealigners is relaxed (step 612). Then, for each path segment with anunacceptable aligner, the process 200 of shaping an aligner is performedwith the new constraints (step 614). If all the aligners are nowacceptable, the overall process 600 exits (step 616).

Aligners may be unacceptable for a variety of reasons, some of which arehandled by the overall process. For example, if any impossible movementswere required (decision step 620), that is, if the shape calculationprocess 200 was required to effect a motion for which no rule oradjustment was available, the process 600 proceeds to execute a modulethat calculates the configuration of a hardware attachment to thesubject tooth to which forces can be applied to effect the requiredmotion (step 640). Because adding hardware can have an effect that ismore than local, when hardware is added to the model, the outer loop ofthe overall process 600 is executed again (step 642).

If no impossible movements were required (“no” branch from step 620),the process transfers control to a path definition process (such as step150, FIG. 2A) to redefine those parts of the treatment path havingunacceptable aligners (step 630). This step can include both changingthe increments of tooth motion, i.e., changing the segmentation, on thetreatment path, changing the path followed by one or more teeth in thetreatment path, or both. After the treatment path has been redefined,the outer loop of the overall process is executed again (step 632). Therecalculation is advantageously limited to recalculating only thosealigners on the redefined portions of the treatment path. If all thealigners are now acceptable, the overall process exits (step 634). Ifunacceptable aligners still remain, the overall process can be repeateduntil an acceptable set of aligners is found or an iteration limit isexceeded (step 650). At this point, as well as at other point in theprocesses that are described in this specification, such as at thecomputation of additional hardware (step 640), the process can interactwith a human operator, such as a clinician or technician, to requestassistance (step 652). Assistance that an operator provides can includedefining or selecting suitable attachments to be attached to a tooth ora bone, defining an added elastic element to provide a needed force forone or more segments of the treatment path, suggesting an alteration tothe treatment path, either in the motion path of a tooth or in thesegmentation of the treatment path, and approving a deviation from orrelaxation of an operative constraint.

As was mentioned above, the overall process 600 is defined andparameterized by various items of input data (step 602). In oneimplementation, this initializing and defining data includes thefollowing items: an iteration limit for the outer loop of the overallprocess; specification of figures of merit that are calculated todetermine whether an aligner is good enough (see FIG. 2, step 270); aspecification of the aligner material; a specification of theconstraints that the shape or configuration of an aligner must satisfyto be acceptable; a specification of the forces and positioning motionsand velocities that are orthodontically acceptable; an initial treatmentpath, which includes the motion path for each tooth and a segmentationof the treatment path into segments, each segment to be accomplished byone aligner; a specification of the shapes and positions of any anchorsinstalled on the teeth or otherwise; and a specification of a model forthe jaw bone and other tissues in or on which the teeth are situated (inthe implementation being described, this model consists of a model of aviscous substrate fluid in which the teeth are embedded and which hasboundary conditions that essentially define a container for the fluid).

FIG. 7 is an exemplary diagram of a statistical root model. As showntherein, using the scanning processes described above, a scanned upperportion 700 of a tooth is identified. The scanned upper portion,including the crown, is then supplemented with a modeled 3D root. The 3Dmodel of the root can be statistically modeled. The 3D model of the root702 and the 3D model of the upper portion 700 together form a complete3D model of a tooth.

FIG. 8 are exemplary diagrams of root modeling, as enhanced usingadditional dental information. In FIG. 8, the additional dentalinformation is X-ray information. An X-ray image 710 of teeth is scannedto provide a 2D view of the complete tooth shapes. An outline of atarget tooth is identified in the X-Ray image. The model 712 asdeveloped in FIG. 7 is modified in accordance with the additionalinformation. In one embodiment, the tooth model of FIG. 7 is morphed toform a new model 714 that conforms with the X-ray data.

FIG. 9 are exemplary diagrams of a CT scan of teeth. In this embodiment,the roots are derived directly from a high-resolution CBCT scan of thepatient. Scanned roots can then be applied to crowns derived from animpression, or used with the existing crowns extracted from Cone BeamComputed Tomography (CBCT) data. A CBCT single scan gives 3D data andmultiple forms of X-ray-like data. PVS impressions are avoided.

In one embodiment, a cone beam x-ray source and a 2D area detector scansthe patient's dental anatomy, preferably over a 360 degree angular rangeand along its entire length, by any one of various methods wherein theposition of the area detector is fixed relative to the source, andrelative rotational and translational movement between the source andobject provides the scanning (irradiation of the object by radiationenergy). As a result of the relative movement of the cone beam source toa plurality of source positions (i.e., “views”) along the scan path, thedetector acquires a corresponding plurality of sequential sets of conebeam projection data (also referred to herein as cone beam data orprojection data), each set of cone beam data being representative ofx-ray attenuation caused by the object at a respective one of the sourcepositions.

FIG. 10 shows an exemplary user interface showing the erupted teeth,while FIG. 11 shows the exemplary diagram of the teeth of FIG. 10 withroot information. Each tooth is individually adjustable using a suitablehandle. In the embodiment of FIGS. 10 and 11, the handle allows anoperator to move the tooth in three-dimensions with six degrees offreedom.

The teeth movement is guided in part using a root-based sequencingsystem. In one embodiment, the movement is constrained by a surface areaconstraint, while in another embodiment, the movement is constrained bya volume constraint.

In one embodiment, the system determines a surface area for each toothmodel. The system then sums all surface areas for all tooth models to bemoved. Next, the system sums all surface areas of all tooth models onthe arch. For each stage of teeth movement, the system checks that apredetermined area ratio or constraint is met while the tooth models aremoved. In one implementation, the constraint can be to ensure that thesurface areas of moving teeth are less than the total surface areas ofteeth on an arch supporting the teeth being moved. If the ratio isgreater than a particular number such as 50%, the system indicates anerror signal to an operator to indicate that the teeth should be movedon a slower basis.

In another embodiment, the system determines the volume for each toothmodel. The system then sums the volumes for all tooth models beingmoved. Next, the system determines the total volume of all tooth modelson the arch. For each stage of teeth movement, the system checks that apredetermined volume ratio or constraint is met while the tooth modelsare moved. In one implementation, the constraint can be to ensure thatthe volume for moving teeth is less than the volume of all teeth on anarch supporting the teeth being moved. If the ratio is greater than aparticular number such as 50%, the system indicates an error signal toan operator to indicate that the teeth should be moved on a slowerbasis.

Optionally, other features are added to the tooth model data sets toproduce desired features in the aligners. For example, it may bedesirable to add digital wax patches to define cavities or recesses tomaintain a space between the aligner and particular regions of the teethor jaw. It may also be desirable to add digital wax patches to definecorrugated or other structural forms to create regions having particularstiffness or other structural properties. In manufacturing processesthat rely on generation of positive models to produce the repositioningappliance, adding a wax patch to the digital model will generate apositive mold that has the same added wax patch geometry. This can bedone globally in defining the base shape of the aligners or in thecalculation of particular aligner shapes. One feature that can be addedis a rim around the gumline, which can be produced by adding a digitalmodel wire at the gumline of the digital model teeth from which thealigner is manufactured. When an aligner is manufactured by pressurefitting polymeric material over a positive physical model of the digitalteeth, the wire along the gumlines causes the aligner to have a rimaround it providing additional stiffness along the gumline.

In another optional manufacturing technique, two sheets of material arepressure fit over the positive tooth model, where one of the sheets iscut along the apex arch of the aligner and the other is overlaid on top.This provides a double thickness of aligner material along the verticalwalls of the teeth.

The changes that can be made to the design of an aligner are constrainedby the manufacturing technique that will be used to produce it. Forexample, if the aligner will be made by pressure fitting a polymericsheet over a positive model, the thickness of the aligner is determinedby the thickness of the sheet. As a consequence, the system willgenerally adjust the performance of the aligner by changing theorientation of the model teeth, the sizes of parts of the model teeth,the position and selection of attachments, and the addition or removalof material (e.g., adding wires or creating dimples) to change thestructure of the aligner. The system can optionally adjust the alignerby specifying that one or more of the aligners are to be made of a sheetof a thickness other than the standard one, to provide more or lessforce to the teeth. On the other hand, if the aligner will be made by astereo lithography process, the thickness of the aligner can be variedlocally, and structural features such as rims, dimples, and corrugationscan be added without modifying the digital model of the teeth.

The system can also be used to model the effects of more traditionalappliances such as retainers and braces and therefore be used togenerate optimal designs and treatment programs for particular patients.

The data processing aspects of the invention can be implemented indigital electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. Data processing apparatus of theinvention can be implemented in a computer program product tangiblyembodied in a machine-readable storage device for execution by aprogrammable processor; and data processing method steps of theinvention can be performed by a programmable processor executing aprogram of instructions to perform functions of the invention byoperating on input data and generating output. The data processingaspects of the invention can be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from and to transmit data and instructions to a datastorage system, at least one input device, and at least one outputdevice. Each computer program can be implemented in a high-levelprocedural or objectoriented programming language, or in assembly ormachine language, if desired; and, in any case, the language can be acompiled or interpreted language. Suitable processors include, by way ofexample, both general and special purpose microprocessors. Generally, aprocessor will receive instructions and data from a read-only memoryand/or a random access memory. Storage devices suitable for tangiblyembodying computer program instructions and data include all forms ofnonvolatile memory, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magnetic diskssuch as internal hard disks and removable disks; magneto-optical disks;and CD-ROM disks. Any of the foregoing can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

To provide for interaction with a user, the invention can be implementedusing a computer system having a display device such as a monitor or LCD(liquid crystal display) screen for displaying information to the userand input devices by which the user can provide input to the computersystem such as a keyboard, a two-dimensional pointing device such as amouse or a trackball, or a three-dimensional pointing device such as adata glove or a gyroscopic mouse. The computer system can be programmedto provide a graphical user interface through which computer programsinteract with users. The computer system can be programmed to provide avirtual reality, three-dimensional display interface.

The invention has been described in terms of particular embodiments.Other embodiments are within the scope of the following claims. Forexample, the steps of the invention can be performed in a differentorder and still achieve desirable results.

1. A computer method comprising: providing a database comprising acompendium of at least one of patient treatment history; orthodontictherapies, orthodontic information and diagnostics; employing a datamining technique for interrogating said database for generating anoutput data stream, the output data stream correlating a patientmalocclusion with an orthodontic treatment; and applying the output datastream to improve a dental appliance or a dental appliance usage.
 2. Themethod of claim 1, further comprising generating a plurality ofappliances having geometries selected to progressively reposition theteeth, wherein the appliances comprise polymeric shells having cavitiesand wherein the cavities of successive shells have different geometriesshaped to receive and resiliently reposition teeth from one arrangementto a successive arrangement.
 3. The method of claim 2, wherein thesequence of appliances includes a sequence of configurations of braces,the braces including brackets and archwires.
 4. The method of claim 2,wherein the sequence of appliances includes a sequence of polymericshells manufactured by fitting polymeric sheets over positive modelscorresponding to the teeth of the patient.
 5. The method of claim 1,wherein the sequence of appliances includes a sequence of polymericshells manufactured by stereo lithography from digital models
 6. Themethod of claim 1, wherein the output data stream is related to clinicalconstraints.
 7. The method of claim 6, wherein the clinical constraintsinclude a maximum rate of displacement of a tooth, a maximum force on atooth, and a desired end position of a tooth.
 8. The method of claim 7,wherein the maximum force is a linear force or a torsional force.
 9. Themethod of claim 7, wherein the maximum rate of displacement is a linearor a angular rate of displacement.
 10. The method of claim 6, whereinthe clinical constraints include a maximum rate of displacement of atooth.
 11. The method of claim 6, wherein the clinical constraintsinclude a maximum rate of linear displacement of a tooth.
 12. The methodof claim 6, wherein the clinical constraints include a maximum rate ofrotational displacement of a tooth.
 13. The method of claim 1, whereinthe last of the sequence of appliances is a positioner for finishing andmaintaining teeth positions.
 14. The method of claim 1, furthercomprising: comparing an actual effect of the appliances with anintended effect of the appliances; and identifying an appliance as anunsatisfactory appliance if the actual effect of the appliance is morethan a threshold different from the intended effect of the appliance andmodifying a model of the unsatisfactory appliance according to theresults of the comparison.
 15. The method of claim 1, further comprisingcapturing at least an initial tooth position, a target tooth position;and one or more intermediate tooth positions.
 16. The method of claim 1,further comprising analyzing one of the intermediate tooth positionswith the target position.
 17. The method of claim 1, further comprisingcapturing characteristics tags associated with a patient case to labelcaptured data.
 18. The method of claim 17, further comprisingaggregating data of a set of treatments based on their tags and ratingthe set of treatments based on the aggregated data.
 19. The method ofclaim 18, further comprising comparing performance of a plurality ofsets of treatments.
 20. The method of claim 1, further comprisingapplying models to calculate risk of treatment complications forindividual patients.
 21. The method of claim 20, further comprisingidentifying a treatment case for special handling.
 22. The method ofclaim 20, further comprising identifying a treatment case for specialtreatment parameters including clinical constraint.
 23. The method ofclaim 20, further comprising clusterizing clinical practitioners bypractice habits.
 24. The method of claim 23, wherein treatmentparameters are adapted to preferences specific to each cluster.
 25. Themethod of claim 1, further comprising applying probabilistic models topredict discrepancies between targeted and actual tooth position atgiven stages in treatment, and where said predictions are calculatedinto treatment plans.